sunoh-kim / convex-optimization

This repository contains my assignment solution for the Convex Optimization course (430.709A_001) offered by Seoul National University (Fall 2018).

Convex Optimization - Assignment Solution

This repository contains my assignment solution for the Convex Optimization course (430.709A_001) offered by Seoul National University (Fall 2018). Based on the library sparse-depth-sensing, which contains MATLAB codes and data for sparse depth sensing, I used a different solver CVX. Sparse depth sensing is the problem of dense depth image reconstruction from the limited amount of measurements. Please refer to the paper Sparse Depth Sensing for Resource-Constrained Robots.

Requirement

• Matlab R2015a or later versions, including
  • the Computer Vision System Toolbox
  • the Robotics System Toolbox

Usage

• run demo_single_frame.m for a demo of the reconstruction algorithm on samples from each individual frame of depth images.
• run demo_multi_frame.m for a demo of the reconstruction algorithm on samples collected across multiple frames, given odometry information.
• run demo_middlebury.m for a demo of the reconstruction algorithm on the Middlebury dataset.

Reference

@article{Ma2017SparseDepthSensing,
  title={Sparse Depth Sensing for Resource-Constrained Robots},
  author={Ma, Fangchang and Carlone, Luca and Ayaz, Ulas and Karaman, Sertac},
  journal={arXiv preprint arXiv:1703.01398},
  year={2017}
}

@inproceedings{ma2016sparse,
  title={Sparse sensing for resource-constrained depth reconstruction},
  author={Ma, Fangchang and Carlone, Luca and Ayaz, Ulas and Karaman, Sertac},
  booktitle={Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on},
  pages={96--103},
  year={2016},
  organization={IEEE}
}